OpenRaiser/Intern-Atlas huggingface dataset:
City traffic forecasting using taxi GPS data: A coarse-grained cellular automata model
Venue: arXiv cs.OH 2016
Year: 2016

City traffic is a dynamic system of enormous complexity. Modeling and predicting city traffic flow remains to be a challenge task and the main difficulties are how to specify the supply and demands and how to parameterize the model. In this paper we attempt to solve these problems with the help of large amount of floating car data. We propose a coarse-grained cellular automata model that simulates vehicles moving on uniform grids whose size are much larger compared with the microscopic cellular automata model. The car-car interaction in the microscopic model is replaced by the coupling between vehicles and coarse-grained state variables in our model. To parameterize the model, flux-occupancy relations are fitted from the historical data at every grids, which serve as the coarse-grained fundamental diagrams coupling the occupancy and speed. To evaluate the model, we feed it with the historical travel demands and trajectories obtained from the floating car data and use the model to predict road speed one hour into the future. Numerical results show that our model can capture the traffic flow pattern of the entire city and make reasonable predictions. The current work can be considered a prototype for a model-based forecasting system for city traffic.
From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur
Venue: arXiv cs.CV 2016
Year: 2016

Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but the extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach is thus that learning the motion flow instead allows the model to focus on the cause of the blur, irrespective of the image content. This is a much easier learning task, but it also avoids the iterative process through which latent image priors are typically applied. Our approach directly estimates the motion flow from the blurred image through a fully-convolutional deep neural network (FCN) and recovers the unblurred image from the estimated motion flow. Our FCN is the first universal end-to-end mapping from the blurred image to the dense motion flow. To train the FCN, we simulate motion flows to generate synthetic blurred-image-motion-flow pairs thus avoiding the need for human labeling. Extensive experiments on challenging realistic blurred images demonstrate that the proposed method outperforms the state-of-the-art.
Realtime Predictive Maintenance with Lambda Architecture
Venue: arXiv cs.DC 2016
Year: 2016

Recently, IoT technologies have been progressed and applications of maintenance area are expected. However, IoT maintenance applications are not spread in Japan yet because of insufficient analysis of real time situation, high cost to collect sensing data and to configure failure detection rules. In this paper, using lambda architecture concept, we propose a maintenance platform in which edge nodes analyze sensing data, detect anomaly, extract a new detection rule in real time and a cloud orders maintenance automatically, also analyzes whole data collected by batch process in detail, updates learning model of edge nodes to improve analysis accuracy.
A note on the triangle inequality for the Jaccard distance
Venue: arXiv cs.DM 2016
Year: 2016

Two simple proofs of the triangle inequality for the Jaccard distance in terms of nonnegative, monotone, submodular functions are given and discussed.
Joint Hand Detection and Rotation Estimation by Using CNN
Venue: arXiv cs.CV 2016
Year: 2016

Hand detection is essential for many hand related tasks, e.g. parsing hand pose, understanding gesture, which are extremely useful for robotics and human-computer interaction. However, hand detection in uncontrolled environments is challenging due to the flexibility of wrist joint and cluttered background. We propose a deep learning based approach which detects hands and calibrates in-plane rotation under supervision at the same time. To guarantee the recall, we propose a context aware proposal generation algorithm which significantly outperforms the selective search. We then design a convolutional neural network(CNN) which handles object rotation explicitly to jointly solve the object detection and rotation estimation tasks. Experiments show that our method achieves better results than state-of-the-art detection models on widely-used benchmarks such as Oxford and Egohands database. We further show that rotation estimation and classification can mutually benefit each other.
A Generalization of the Curry-Howard Correspondence
Venue: arXiv cs.LO 2016
Year: 2016

We present a variant of the calculus of deductive systems developed in (Lambek 1972, 1974), and give a generalization of the Curry-Howard-Lambek theorem giving an equivalence between the category of typed lambda-calculi and the category of cartesian closed categories and exponential-preserving morphisms that leverages the theory of generalized categories (Schoenbaum 2016). We discuss potential applications and extensions.
Fast Fourier single-pixel imaging using binary illumination
Venue: arXiv cs.CV 2016
Year: 2016

Fourier single-pixel imaging (FSI) has proven capable of reconstructing high-quality two-dimensional and three-dimensional images. The utilization of the sparsity of natural images in Fourier domain allows high-resolution images to be reconstructed from far fewer measurements than effective image pixels. However, applying original FSI in digital micro-mirror device (DMD) based high-speed imaging system turns out to be challenging, because the original FSI uses grayscale Fourier basis patterns for illumination while DMDs generate grayscale patterns at a relatively low rate. DMDs are a binary device which can only generate a black-and-white pattern at each instance. In this paper, we adopt binary Fourier patterns for illumination to achieve DMD-based high-speed single-pixel imaging. Binary Fourier patterns are generated by upsampling and then applying error diffusion based dithering to the grayscale patterns. Experiments demonstrate the proposed technique able to achieve static imaging with high quality and dynamic imaging in real time. The proposed technique potentially allows high-quality and high-speed imaging over broad wavebands.
A Review of Intelligent Practices for Irrigation Prediction
Venue: arXiv cs.LG 2016
Year: 2016

Population growth and increasing droughts are creating unprecedented strain on the continued availability of water resources. Since irrigation is a major consumer of fresh water, wastage of resources in this sector could have strong consequences. To address this issue, irrigation water management and prediction techniques need to be employed effectively and should be able to account for the variabilities present in the environment. The different techniques surveyed in this paper can be classified into two categories: computational and statistical. Computational methods deal with scientific correlations between physical parameters whereas statistical methods involve specific prediction algorithms that can be used to automate the process of irrigation water prediction. These algorithms interpret semantic relationships between the various parameters of temperature, pressure, evapotranspiration etc. and store them as numerical precomputed entities specific to the conditions and the area used as the data for the training corpus used to train it. We focus on reviewing the computational methods used to determine Evapotranspiration and its implications. We compare the efficiencies of different data mining and machine learning methods implemented in this area, such as Logistic Regression, Decision Tress Classifier, SysFor, Support Vector Machine(SVM), Fuzzy Logic techniques, Artifical Neural Networks(ANNs) and various hybrids of Genetic Algorithms (GA) applied to irrigation prediction. We also recommend a possible technique for the same based on its superior results in other such time series analysis tasks.
A Revolutionary Rural Agricultural Participatory Sensing Approach Using Delay Tolerant Networks
Venue: arXiv cs.NI 2016
Year: 2016

To provide sustainable digital agro-advisory services to farmers, seamless flow of information from the farmers to the experts/expert systems, and vice versa is required. The query generated by the farmers, which may contain multimedia data regarding disease or pest attack in the crops is required to be transmitted to the experts for analysis. Further, after analyzing the query, an alert or advice from the expert system is required to be communicated back to the farmers within some tolerable delay. However, in a country like India, network connectivity is extremely poor in several agricultural regions which makes the end-to-end connectivity between the farmers and the expert system intermittent. Therefore, providing agro-advisory services to farmers in a reasonable time becomes a challenge. In this paper, we propose a Delay Tolerant Network (DTN) based relay application model which enables agro-advisory services to farmers located in \emph{No-network} or \emph{Poor-network} zones. In the proposed model, end-to-end communication has been enabled with the help of Device to Device (D2D) communication and by introducing mobile relay nodes which can carry the queries (responses), from (to) the poor or no network zones to (from) the zones where communication is possible. Implementation of this model has been presented for the tea farmers of West Bengal and Assam, which can be extended for various other applications in future.
Tuple spaces implementations and their efficiency
Venue: arXiv cs.DC 2016
Year: 2016

Among the paradigms for parallel and distributed computing, the one popularized with Linda, and based on tuple spaces, is one of the least used, despite the fact of being intuitive, easy to understand and to use. A tuple space is a repository, where processes can add, withdraw or read tuples by means of atomic operations. Tuples may contain different values, and processes can inspect their content via pattern matching. The lack of a reference implementation for this paradigm has prevented its widespread. In this paper, first we perform an extensive analysis of a number of actual implementations of the tuple space paradigm and summarise their main features. Then, we select four such implementations and compare their performances on four different case studies that aim at stressing different aspects of computing such as communication, data manipulation, and cpu usage. After reasoning on strengths and weaknesses of the four implementations, we conclude with some recommendations for future work towards building an effective implementation of the tuple space paradigm.
CoALP-Ty'16
Venue: arXiv cs.LO 2016
Year: 2016

This volume constitutes the pre-proceedings of the Workshop on Coalgebra, Horn Clause Logic Programming and Types (CoALP-Ty'16), held on 28--29 November 2016 in Edinburgh as a mark of the end of the EPSRC Grant Coalgebraic Logic Programming for Type Inference, by E. Komendantskaya and J. Power. This volume consists of extended abstracts describing current research in the following areas: Semantics: Lawvere theories and Coalgebra in Logic and Functional Programming Programming languages: Horn Clause Logic for Type Inference in Functional Languages and Beyond After discussion at the workshop authors of the extended abstracts will be invited to submit a full paper to go through a second round of refereeing and selection for the formal proceedings.
Monet: A User-oriented Behavior-based Malware Variants Detection System for Android
Venue: arXiv cs.CR 2016
Year: 2016

Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around one million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact "derivatives" (or variants) from existing malware families. In this paper, we first show that runtime behaviors of malware's core functionalities are in fact similar within a malware family. Hence, we propose a framework to combine "runtime behavior" with "static structures" to detect malware variants. We present the design and implementation of MONET, which has a client and a backend server module. The client module is a lightweight, in-device app for behavior monitoring and signature generation, and we realize this using two novel interception techniques. The backend server is responsible for large scale malware detection. We collect 3723 malware samples and top 500 benign apps to carry out extensive experiments of detecting malware variants and defending against malware transformation. Our experiments show that MONET can achieve around 99% accuracy in detecting malware variants. Furthermore, it can defend against 10 different obfuscation and transformation techniques, while only incurs around 7% performance overhead and about 3% battery overhead. More importantly, MONET will automatically alert users with intrusion details so to prevent further malicious behaviors.
Technical Report: A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
Venue: arXiv cs.LG 2016
Year: 2016

Sparsity-constrained optimization is an important and challenging problem that has wide applicability in data mining, machine learning, and statistics. In this paper, we focus on sparsity-constrained optimization in cases where the cost function is a general nonlinear function and, in particular, the sparsity constraint is defined by a graph-structured sparsity model. Existing methods explore this problem in the context of sparse estimation in linear models. To the best of our knowledge, this is the first work to present an efficient approximation algorithm, namely, Graph-structured Matching Pursuit (Graph-Mp), to optimize a general nonlinear function subject to graph-structured constraints. We prove that our algorithm enjoys the strong guarantees analogous to those designed for linear models in terms of convergence rate and approximation accuracy. As a case study, we specialize Graph-Mp to optimize a number of well-known graph scan statistic models for the connected subgraph detection task, and empirical evidence demonstrates that our general algorithm performs superior over state-of-the-art methods that are designed specifically for the task of connected subgraph detection.
Rangzen: Anonymously Getting the Word Out in a Blackout
Venue: arXiv cs.NI 2016
Year: 2016

In recent years governments have shown themselves willing to impose blackouts to shut off key communication infrastructure during times of civil strife, and to surveil citizen communications whenever possible. However, it is exactly during such strife that citizens need reliable and anonymous communications the most. In this paper, we present Rangzen, a system for anonymous broadcast messaging during network blackouts. Rangzen is distinctive in both aim and design. Our aim is to provide an anonymous, one-to-many messaging layer that requires only users' smartphones and can withstand network-level attacks. Our design is a delay-tolerant mesh network which deprioritizes adversarial messages by means of a social graph while preserving user anonymity. We built a complete implementation that runs on Android smartphones, present benchmarks of its performance and battery usage, and present simulation results suggesting Rangzen's efficacy at scale.
CSMA-based Packet Broadcast in Radio Channels with Hidden Stations
Venue: arXiv cs.NI 2016
Year: 2016

Carrier Sense Multiple Access (CSMA) MAC protocols are known to suffer from the hidden station (HS) problem. The complete mathematical analysis of CSMA networks with HSs is still an open problem, even for broadcast communication with a simple linear network topology. In this paper we address this challenge by introducing a MAC layer modeling methodology based on time- and space-domain Markov processes. Using this methodology we derive the closed-form solution for the steady-state performance in infinite one-dimensional (1-D) CSMA networks with HSs. The analytical results are validated by simulation and establish that: 1) under the assumption of fixed frame duration, if the conditional channel access probability at each station exceeds a certain threshold, the CSMA system enters the status of "synchronized transmissions", where a large number of adjacent stations transmit in overlap and interfere each other resulting in null system goodput. 2) The maximum system goodput of CSMA broadcast communication increases with increased station density but becomes increasingly sensitive to the conditional channel access probability. In [25] we validate the analytical results gained in this paper by simulation of a multi-lane highway scenario, and provide quantitative guidance for congestion control algorithms in vehicular networks.
Unraveling reported dreams with text analytics
Venue: arXiv cs.CL 2016
Year: 2016

We investigate what distinguishes reported dreams from other personal narratives. The continuity hypothesis, stemming from psychological dream analysis work, states that most dreams refer to a person's daily life and personal concerns, similar to other personal narratives such as diary entries. Differences between the two texts may reveal the linguistic markers of dream text, which could be the basis for new dream analysis work and for the automatic detection of dream descriptions. We used three text analytics methods: text classification, topic modeling, and text coherence analysis, and applied these methods to a balanced set of texts representing dreams, diary entries, and other personal stories. We observed that dream texts could be distinguished from other personal narratives nearly perfectly, mostly based on the presence of uncertainty markers and descriptions of scenes. Important markers for non-dream narratives are specific time expressions and conversational expressions. Dream texts also exhibit a lower discourse coherence than other personal narratives.
Empirical Evaluation of A New Approach to Simplifying Long Short-term Memory (LSTM)
Venue: arXiv cs.NE 2016
Year: 2016

The standard LSTM, although it succeeds in the modeling long-range dependences, suffers from a highly complex structure that can be simplified through modifications to its gate units. This paper was to perform an empirical comparison between the standard LSTM and three new simplified variants that were obtained by eliminating input signal, bias and hidden unit signal from individual gates, on the tasks of modeling two sequence datasets. The experiments show that the three variants, with reduced parameters, can achieve comparable performance with the standard LSTM. Due attention should be paid to turning the learning rate to achieve high accuracies
A Note on Testing Intersection of Convex Sets in Sublinear Time
Venue: arXiv cs.DS 2016
Year: 2016

We present a simple sublinear time algorithm for testing the following geometric property. Let $P_1, ..., P_n$ be $n$ convex sets in $\mathbb{R}^d$ ($n \gg d$), such as polytopes, balls, etc. We assume that the complexity of each set depends only on $d$ (and not on the number of sets $n$). We test the property that there exists a common point in all sets, i.e. that their intersection is nonempty. Our goal is to distinguish between the case where the intersection is nonempty, and the case where even after removing many of the sets the intersection is empty. In particular, our algorithm returns PASS if all of the $n$ sets intersect, and returns FAIL with probability at least $1-ε$ if no point belongs to $\fracα{d+1} n$ sets, for any given $0 < α, ε< 1$.
Fault Attacks on Encrypted General Purpose Compute Platforms
Venue: arXiv cs.CR 2016
Year: 2016

Adversaries with physical access to a target platform can perform cold boot or DMA attacks to extract sensitive data from the RAM. In response, several main-memory encryption schemes have been proposed to prevent such attacks. Also hardware vendors have acknowledged the threat and already announced respective hardware extensions. Intel's SGX and AMD's SME will provide means to encrypt parts of the RAM to protect security-relevant assets that reside there. Encrypting the RAM will protect the user's content against passive eavesdropping. However, the level of protection it provides in scenarios that involve an adversary who is not only able to read from RAM but can also change content in RAM is less clear. Obviously, encryption offers some protection against such an "active" adversary: from the ciphertext the adversary cannot see what value is changed in the plaintext, nor predict the system behaviour based on the changes. But is this enough to prevent an active adversary from performing malicious tasks? This paper addresses the open research question whether encryption alone is a dependable protection mechanism in practice when considering an active adversary. To this end, we first build a software based memory encryption solution on a desktop system which mimics AMD's SME. Subsequently, we demonstrate a proof-of-concept fault attack on this system, by which we are able to extract the private RSA key of a GnuPG user. Our work suggests that transparent memory encryption is not enough to prevent active attacks.
SimTensor: A synthetic tensor data generator
Venue: arXiv cs.MS 2016
Year: 2016

SimTensor is a multi-platform, open-source software for generating artificial tensor data (either with CP/PARAFAC or Tucker structure) for reproducible research on tensor factorization algorithms. SimTensor is a stand-alone application based on MATALB. It provides a wide range of facilities for generating tensor data with various configurations. It comes with a user-friendly graphical user interface, which enables the user to generate tensors with complicated settings in an easy way. It also has this facility to export generated data to universal formats such as CSV and HDF5, which can be imported via a wide range of programming languages (C, C++, Java, R, Fortran, MATLAB, Perl, Python, and many more). The most innovative part of SimTensor is this that can generate temporal tensors with periodic waves, seasonal effects and streaming structure. it can apply constraints such as non-negativity and different kinds of sparsity to the data. SimTensor also provides this facility to simulate different kinds of change-points and inject various types of anomalies. The source code and binary versions of SimTensor is available for download in http://www.simtensor.org.
Depth-Based Visual Servoing Using Low-Accurate Arm
Venue: arXiv cs.RO 2016
Year: 2016

This paper proposes a visual-servoing method dedicated to grasping of daily-life objects. In order to obtain an affordable solution, we use a low-accurate robotic arm. Our method corrects errors by using an RGB-D sensor. It is based on SURF invariant features which allows us to perform object recognition at a high frame rate. We define regions of interest based on depth segmentation, and we use them to speed-up the recognition and to improve reliability. The system has been tested on a real-world scenario. In spite of the lack of accuracy of all the components and the uncontrolled environment, it grasps objects successfully on more than 95 percents of the trials.